Empirically, the network performance does not increase much for a fully-connected network on MNIST when you add layers, but you can probably find ways to improve it on networks with 3+ hidden layers, such as data augmentation (e.g. variations of all inputs translated +-0..2 pixels in x and y, roughly 25 times the original data size, as a start).
I don't think this idea is pursued very far in practice, because CNNs offer a much better performance increase for the effort required. You hit the point of diminishing returns earlier with a basic MLP (around 96-97% accuracy) than you can reach easily with a CNN (around 99% accuracy).
The theory basis for this difference is not obvious to me, but very likely yes this is related to over-fitting. The weight sharing and feature pooling in a CNN is very effective way of processing image data for classification tasks, and avoids over-fitting by reducing the number of parameters, whilst re-using the parameters for the task in a way that makes very good sense given the nature of the inputs.